continuous-training

🧹 Automated Filtering of Undesirable Web Data to Update LLM Knowledge

Open-Source Machine Learning Platform

Data version control with Makefile and DVC for a regression task to estimate insurance costs for certain individuals.

🏷️ An AI-driven approach to Label LLM Training Data

mini project

The runtime environment for the KOI-System. Train models, run instances, and collect samples.

The backend of the KOI-system.

Fully automated MLOps pipeline with continuous training, drift detection, model versioning, and self-healing deployments using canary releases and real-time monitoring.

🌱 Manifeste de la Clairveillance : pour des institutions qui mesurent, doutent et apprennent

End-to-end Financial MLOps pipeline for automated stock forecasting featuring PyTorch LSTM, Apache Airflow orchestration, MLflow tracking, MongoDB Atlas, and FastAPI containerized with Docker and GHCR.

A production-grade, end-to-end MLOps platform designed to automate the complete lifecycle of a financial fraud detection system. This repository demonstrates how to transition from a static ML model to a self-healing, automated production system.

Great Expectations gating data at the pull request, DVC versioning datasets in S3, and models landing as PendingManualApproval behind an IAM action CI does not hold: continuous training with a human in the loop. A Go CLI drives SageMaker training and the Model Registry from GitHub Actions over OIDC, on Terraform with no idle compute.

Closed-loop MLOps control plane: versioned data, gated training, canary releases, drift detection, automated retraining and rollback.

A concurrent training and generation pipeline leveraging active learning to drive synthetic data rendering. By generating customized datasets simultaneously alongside model training, it creates a real-time feedback loop to dynamically refine object detection models.

This project integrates Airflow, EC2, MLFlow, and MLOps principles to deploy a robust pipeline for classifying medical MNIST images. Streamlit enables user-friendly image uploads, MLFlow handles model registry and inference, while Airflow automates data updates and model retraining on AWS EC2.

Closed-loop MLOps pipeline for demand forecasting: FastAPI serving with prediction logging, Airflow monitoring a 14-day rolling RMSE to trigger retraining, leak-free chronological validation, and a promotion audit so a new model ships only if it beats production.

Enterprise-grade Self-Healing MLOps Pipeline with Hybrid Decision Engine (Rules + Contextual Bandits), Automated Drift Monitoring, Airflow, and K8s Zero-Downtime Rollouts